380 lines
13 KiB
Python
380 lines
13 KiB
Python
from fastapi import APIRouter, BackgroundTasks, Depends, HTTPException
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from fastapi.responses import RedirectResponse
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from pydantic import BaseModel
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from app.core.database import get_session
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from app.repositories.resume_repository import ResumeRepository
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from celery_worker.interview_analysis_task import (
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analyze_multiple_candidates,
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generate_interview_report,
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)
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router = APIRouter(prefix="/analysis", tags=["analysis"])
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class AnalysisResponse(BaseModel):
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"""Ответ запуска задачи анализа"""
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message: str
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resume_id: int
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task_id: str
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class BulkAnalysisRequest(BaseModel):
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"""Запрос массового анализа"""
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resume_ids: list[int]
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class BulkAnalysisResponse(BaseModel):
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"""Ответ массового анализа"""
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message: str
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resume_count: int
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task_id: str
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class CandidateRanking(BaseModel):
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"""Рейтинг кандидата"""
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resume_id: int
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candidate_name: str
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overall_score: int
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recommendation: str
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position: str
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@router.post("/interview-report/{resume_id}", response_model=AnalysisResponse)
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async def start_interview_analysis(
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resume_id: int,
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background_tasks: BackgroundTasks,
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resume_repo: ResumeRepository = Depends(ResumeRepository),
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):
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"""
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Запускает анализ интервью для конкретного кандидата
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Анализирует:
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- Соответствие резюме вакансии
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- Качество ответов в диалоге интервью
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- Технические навыки и опыт
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- Коммуникативные способности
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- Общую рекомендацию и рейтинг
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"""
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# Проверяем, существует ли резюме
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resume = await resume_repo.get_by_id(resume_id)
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if not resume:
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raise HTTPException(status_code=404, detail="Resume not found")
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# Запускаем задачу анализа
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task = generate_interview_report.delay(resume_id)
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return AnalysisResponse(
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message="Interview analysis started", resume_id=resume_id, task_id=task.id
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)
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@router.post("/bulk-analysis", response_model=BulkAnalysisResponse)
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async def start_bulk_analysis(
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request: BulkAnalysisRequest,
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background_tasks: BackgroundTasks,
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resume_repo: ResumeRepository = Depends(ResumeRepository),
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):
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"""
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Запускает массовый анализ нескольких кандидатов
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Возвращает ранжированный список кандидатов по общему баллу
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Полезно для сравнения кандидатов на одну позицию
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"""
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# Проверяем, что все резюме существуют
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existing_resumes = []
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for resume_id in request.resume_ids:
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resume = await resume_repo.get_by_id(resume_id)
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if resume:
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existing_resumes.append(resume_id)
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if not existing_resumes:
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raise HTTPException(status_code=404, detail="No valid resumes found")
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# Запускаем задачу массового анализа
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task = analyze_multiple_candidates.delay(existing_resumes)
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return BulkAnalysisResponse(
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message="Bulk analysis started",
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resume_count=len(existing_resumes),
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task_id=task.id,
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)
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@router.get("/ranking/{vacancy_id}")
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async def get_candidates_ranking(
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vacancy_id: int, resume_repo: ResumeRepository = Depends(ResumeRepository)
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):
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"""
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Получить ранжированный список кандидатов для вакансии
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Сортирует кандидатов по результатам анализа интервью
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Показывает только тех, кто прошел интервью
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"""
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# Получаем все резюме для вакансии со статусом "interviewed"
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resumes = await resume_repo.get_by_vacancy_id(vacancy_id)
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interviewed_resumes = [r for r in resumes if r.status in ["interviewed"]]
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if not interviewed_resumes:
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return {
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"vacancy_id": vacancy_id,
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"candidates": [],
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"message": "No interviewed candidates found",
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}
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# Запускаем массовый анализ если еще не было
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resume_ids = [r.id for r in interviewed_resumes]
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task = analyze_multiple_candidates.delay(resume_ids)
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# В реальности здесь нужно дождаться выполнения или получить из кэша
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# Пока возвращаем информацию о запущенной задаче
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return {
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"vacancy_id": vacancy_id,
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"task_id": task.id,
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"message": f"Analysis started for {len(resume_ids)} candidates",
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"resume_ids": resume_ids,
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}
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@router.get("/report/{resume_id}")
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async def get_interview_report(
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resume_id: int, resume_repo: ResumeRepository = Depends(ResumeRepository)
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):
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"""
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Получить готовый отчет анализа интервью
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Если отчет еще не готов - запускает анализ
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"""
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resume = await resume_repo.get_by_id(resume_id)
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if not resume:
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raise HTTPException(status_code=404, detail="Resume not found")
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# Проверяем, есть ли уже готовый отчет в notes
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if resume.notes and "ОЦЕНКА КАНДИДАТА" in resume.notes:
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return {
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"resume_id": resume_id,
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"candidate_name": resume.applicant_name,
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"status": "completed",
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"report_summary": resume.notes,
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"message": "Report available",
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}
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# Если отчета нет - запускаем анализ
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task = generate_interview_report.delay(resume_id)
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return {
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"resume_id": resume_id,
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"candidate_name": resume.applicant_name,
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"status": "in_progress",
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"task_id": task.id,
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"message": "Analysis started, check back later",
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}
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@router.get("/statistics/{vacancy_id}")
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async def get_analysis_statistics(
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vacancy_id: int, resume_repo: ResumeRepository = Depends(ResumeRepository)
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):
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"""
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Получить статистику анализа кандидатов по вакансии
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"""
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resumes = await resume_repo.get_by_vacancy_id(vacancy_id)
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total_candidates = len(resumes)
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interviewed = len([r for r in resumes if r.status == "interviewed"])
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with_reports = len(
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[r for r in resumes if r.notes and "ОЦЕНКА КАНДИДАТА" in r.notes]
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)
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# Подсчитываем рекомендации из notes (упрощенно)
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recommendations = {
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"strongly_recommend": 0,
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"recommend": 0,
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"consider": 0,
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"reject": 0,
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}
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for resume in resumes:
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if resume.notes and "ОЦЕНКА КАНДИДАТА" in resume.notes:
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notes = resume.notes.lower()
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if "strongly_recommend" in notes:
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recommendations["strongly_recommend"] += 1
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elif "recommend" in notes and "strongly_recommend" not in notes:
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recommendations["recommend"] += 1
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elif "consider" in notes:
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recommendations["consider"] += 1
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elif "reject" in notes:
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recommendations["reject"] += 1
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return {
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"vacancy_id": vacancy_id,
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"statistics": {
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"total_candidates": total_candidates,
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"interviewed_candidates": interviewed,
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"analyzed_candidates": with_reports,
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"recommendations": recommendations,
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"analysis_completion": round((with_reports / max(interviewed, 1)) * 100, 1)
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if interviewed > 0
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else 0,
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},
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}
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@router.get("/pdf-report/{resume_id}")
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async def get_pdf_report(
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resume_id: int,
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session=Depends(get_session),
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resume_repo: ResumeRepository = Depends(ResumeRepository),
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):
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"""
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Получить PDF отчет по интервью
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Если отчет готов - перенаправляет на S3 URL
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Если отчета нет - возвращает информацию о статусе
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"""
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from sqlmodel import select
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from app.models.interview import InterviewSession
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from app.models.interview_report import InterviewReport
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# Проверяем, существует ли резюме
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resume = await resume_repo.get_by_id(resume_id)
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if not resume:
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raise HTTPException(status_code=404, detail="Resume not found")
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# Ищем сессию интервью и отчет
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statement = (
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select(InterviewReport, InterviewSession)
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.join(
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InterviewSession,
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InterviewReport.interview_session_id == InterviewSession.id,
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)
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.where(InterviewSession.resume_id == resume_id)
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)
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result = await session.exec(statement)
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report_session = result.first()
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if not report_session:
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# Если отчета нет - возможно, нужно запустить анализ
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raise HTTPException(
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status_code=404,
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detail="Interview report not found. Run analysis first using POST /analysis/interview-report/{resume_id}",
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)
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report, interview_session = report_session
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if not report.pdf_report_url:
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# PDF еще не сгенерирован
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return {
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"status": "pdf_not_ready",
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"message": "PDF report is being generated or failed to generate",
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"report_id": report.id,
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"candidate_name": resume.applicant_name,
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}
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# Перенаправляем на S3 URL
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return RedirectResponse(url=report.pdf_report_url, status_code=302)
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@router.get("/report-data/{resume_id}")
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async def get_report_data(
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resume_id: int,
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session=Depends(get_session),
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resume_repo: ResumeRepository = Depends(ResumeRepository),
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):
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"""
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Получить данные отчета в JSON формате (без PDF)
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"""
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from sqlmodel import select
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from app.models.interview import InterviewSession
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from app.models.interview_report import InterviewReport
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# Проверяем, существует ли резюме
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resume = await resume_repo.get_by_id(resume_id)
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if not resume:
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raise HTTPException(status_code=404, detail="Resume not found")
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# Ищем отчет
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statement = (
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select(InterviewReport, InterviewSession)
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.join(
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InterviewSession,
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InterviewReport.interview_session_id == InterviewSession.id,
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)
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.where(InterviewSession.resume_id == resume_id)
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)
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result = await session.exec(statement)
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report_session = result.first()
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if not report_session:
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raise HTTPException(status_code=404, detail="Interview report not found")
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report, interview_session = report_session
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return {
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"report_id": report.id,
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"candidate_name": resume.applicant_name,
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"position": "Unknown Position", # Можно расширить через vacancy
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"interview_date": report.created_at.isoformat(),
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"overall_score": report.overall_score,
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"recommendation": report.recommendation.value,
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"scores": {
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"technical_skills": {
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"score": report.technical_skills_score,
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"justification": report.technical_skills_justification,
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"concerns": report.technical_skills_concerns,
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},
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"experience_relevance": {
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"score": report.experience_relevance_score,
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"justification": report.experience_relevance_justification,
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"concerns": report.experience_relevance_concerns,
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},
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"communication": {
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"score": report.communication_score,
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"justification": report.communication_justification,
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"concerns": report.communication_concerns,
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},
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"problem_solving": {
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"score": report.problem_solving_score,
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"justification": report.problem_solving_justification,
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"concerns": report.problem_solving_concerns,
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},
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"cultural_fit": {
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"score": report.cultural_fit_score,
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"justification": report.cultural_fit_justification,
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"concerns": report.cultural_fit_concerns,
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},
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},
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"strengths": report.strengths,
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"weaknesses": report.weaknesses,
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"red_flags": report.red_flags,
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"next_steps": report.next_steps,
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"metrics": {
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"interview_duration_minutes": report.interview_duration_minutes,
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"dialogue_messages_count": report.dialogue_messages_count,
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"questions_quality_score": report.questions_quality_score,
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},
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"pdf_available": bool(report.pdf_report_url),
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"pdf_url": report.pdf_report_url,
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"analysis_metadata": {
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"method": report.analysis_method,
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"model_used": report.llm_model_used,
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"analysis_duration": report.analysis_duration_seconds,
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},
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}
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